Abstract
Summary: We propose a three-step periodicity detection algorithm named LSPR. Our method first preprocesses the raw time-series by removing the linear trend and filtering noise. In the second step, LSPR employs a Lomb-Scargle periodogram to estimate the periodicity in the time-series. Finally, harmonic regression is applied to model the cyclic components. Inferred periodic transcripts are selected by a false discovery rate procedure. We have applied LSPR to unevenly sampled synthetic data and two Arabidopsis diurnal expression datasets, and compared its performance with the existing well-established algorithms. Results show that LSPR is capable of identifying periodic transcripts more accurately than existing algorithms.
Original language | English (US) |
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Article number | btr041 |
Pages (from-to) | 1023-1025 |
Number of pages | 3 |
Journal | Bioinformatics |
Volume | 27 |
Issue number | 7 |
DOIs | |
State | Published - Apr 2011 |
Externally published | Yes |
Bibliographical note
Funding Information:Funding: Ministry of Science and Technology of China (2006CB100105); College Student Research and Career-creation Program of Beijing (2010).